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https://github.com/codesue/kelly

📝 The Swedish Kelly list as a Hugging Face dataset
https://github.com/codesue/kelly

cefr dataset lexicon spraakbanken swedish

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📝 The Swedish Kelly list as a Hugging Face dataset

Awesome Lists containing this project

README 

        
---

annotations_creators:

- expert-generated

language:

- sv

language_creators:

- expert-generated

license:

- cc-by-4.0

multilinguality:

- monolingual

pretty_name: kelly

size_categories:

- 1K The common noun "dag" ("day") has a rank of 190 in the list. It was used 117,835

times in SweWaC, meaning it occured 1033.61 times per million words. This word

is among the most important vocabulary words for Swedish language learners and

should be learned at the A1 CEFR level. An example usage of this word is the

phrase "god dag" ("good day").



### Data Fields



- `id`: The row number for the data entry, starting at 1. Generally corresponds

  to the rank of the word.

- `raw_frequency`: The raw frequency of the word.

- `relative_frequency`: The relative frequency of the word measured in

  number of occurences per million words.

- `cefr_level`: The CEFR level (A1, A2, B1, B2, C1, C2) of the word.

- `source`: Whether the word came from SweWaC, translation lists (T2), or

  was manually added (manual).

- `marker`: The grammatical marker of the word, if any, such as an article or

  infinitive marker.

- `lemma`: The lemma of the word, sometimes provided with its spelling or

  stylistic variants.

- `pos`: The word's part-of-speech.

- `examples`: Usage examples and comments. Only available for some of the words.



Manual entries were prepended to the list, giving them a higher rank than they

might otherwise have had. For example, the manual entry "Göteborg ("Gothenberg")

has a rank of 20, while the first non-manual entry "och" ("and") has a rank of

87. However, a conjunction and common stopword is far more likely to occur than

the name of a city.



### Data Splits



There is a single split, `train`.



## Dataset Creation



Please refer to the article [Corpus-based approaches for the creation of a frequency

based vocabulary list in the EU project KELLY – issues on reliability, validity and

coverage](https://gup.ub.gu.se/publication/148533?lang=en) for information about how

the original dataset was created and considerations for using the data.



**The following changes have been made to the original dataset**:



- Changed header names.

- Normalized the large web-acquired corpus name to "SweWac" in the `source` field.

- Set the relative frequency of manual entries to null rather than 1000000.



## Additional Information



### Licensing Information



[CC BY 4.0](https://creativecommons.org/licenses/by/4.0)



### Citation Information



Please cite the authors if you use this dataset in your work:



```bibtex

@article{Kilgarriff2013,

  doi = {10.1007/s10579-013-9251-2},

  url = {https://doi.org/10.1007/s10579-013-9251-2},

  year = {2013},

  month = sep,

  publisher = {Springer Science and Business Media {LLC}},

  volume = {48},

  number = {1},

  pages = {121--163},

  author = {Adam Kilgarriff and Frieda Charalabopoulou and Maria Gavrilidou and Janne Bondi Johannessen and Saussan Khalil and Sofie Johansson Kokkinakis and Robert Lew and Serge Sharoff and Ravikiran Vadlapudi and Elena Volodina},

  title = {Corpus-based vocabulary lists for language learners for nine languages},

  journal = {Language Resources and Evaluation}

}

```



### Contributions



Thanks to [@spraakbanken](https://github.com/spraakbanken) for creating this dataset

and to [@codesue](https://github.com/codesue) for adding it.